FLUXCOM-X daily net ecosystem exchange on global 0.25 degree grid for 2015

X-BASE NEE is based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce this global product. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorlogical data, plant functional type classification as well as MODIS based vegitation indicies and land surface temperature. XGBoost was used as the machine learning algorithm. The GPP estimates from the eddy covariance data was based on the Nighttime Partitioning method.

Published paper: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-165/
Nelson, J.A., Walther, S., Jung, M., Gans, F., Kraft, B., Weber, U., Hamdi, Z., Duveiller, G., Zhang, W., 2023. FLUXCOM-X-BASE. https://doi.org/10.18160/5NZG-JMJE

X-BASE NEE 基于FLUXCOM-X框架构建，该框架通过对现场涡度协方差数据进行机器学习模型训练，并利用这些模型生成全球产品。X-BASE实验作为FLUXCOM-X框架的基础配置，旨在作为基准，其中包含核心气象数据、植物功能型分类、基于MODIS的植被指数和地表温度作为预测因子。在此次研究中，采用了XGBoost作为机器学习算法。涡度协方差数据的GPP估算基于夜间分割方法。

已发表论文：https://egusphere.copernicus.org/preprints/2024/egusphere-2024-165/
Nelson, J.A., Walther, S., Jung, M., Gans, F., Kraft, B., Weber, U., Hamdi, Z., Duveiller, G., Zhang, W., 2023. FLUXCOM-X-BASE. https://doi.org/10.18160/5NZG-JMJE